Compiler Optimization-Space
Exploration
Authors
Spyridon Triantafyllis, Manish Vachharajani,
Neil Vachharajani, David I. August
Adrian Pop
IDA/PELAB
adrpo@ida.liu.se
Outline
 Introduction
 The Problem:
 Predictive Heuristics and A Priori Evaluation
 Some Solutions:
 Iterative Compilation and A Posteriori Evaluation
 Our Solution
 Optimization-Space Exploration
 Evaluation
 Conclusion
November 11, 2003
2
Introduction
 Processors
 become more complex
 incorporate additional computational resources
 Consequence
 Compilers
 become more complex
 use aggressive optimizations
 have to use predictive heuristics in order to decide
where and to what extend optimizations should be
applied
November 11, 2003
3
The Problem: Predictive Heuristics
 Predictive Heuristics
 tries to determine a priori the benefits of
certain optimization
 are tuned to give the highest average
performance
 The Result
 significant performance gains are unrealized!
November 11, 2003
4
Some Solutions: Iterative Compilation
 Iterative Compilation
 optimize the programs in many ways
 choose a posteriori the best code version
 Pitfall of current schemes
 prohibitive compilation times!
 limitation to specific architectures
 embedded systems
 limited to specific optimizations
November 11, 2003
5
Our solution: Optimization-Space Exploration
 OSE Compiler (Practical Iterative Compilation)
 explores the space of optimization
configurations through multiple compilations
 it uses the experience of the compiler writer
to prune the number of configurations that
should be explored
 uses a performance estimator to not evaluate
the code by execution
 selects a custom configuration for each code
segment
 selects next optimization configuration by
examining the previous configurations
characteristics
November 11, 2003
6
OSE over many conigurations
November 11, 2003
7
OSE – Limiting the Search Space
 Optimization Space
 derived from a set of optimization parameters
 Optimization Parameters








Optimization level
High Level Optimization (HLO) level
Micro-architecture type
Coalesce adjacent loads and stores
HLO phase order
Loop unroll limit
Update dependencies after unrolling
Perform software pipelining
November 11, 2003
8
OSE – Limiting the Search Space
 Optimization Parameters
 Heuristic to disable software pipelining
 Allow control speculation during software
pipelining
 Software pipeline outer loops
 Enable if-conversion heuristic for software
pipelining
 Software pipeline loops with early exists
 Enable if conversion
 Enable non-standard predication
 Enable pre-scheduling
 Scheduler ready criterion
November 11, 2003
9
OSE – Limiting the Search Space
 Compiler Construction-time Pruning
 limit the total number of configurations that will be
considered at compile time
 construct a set S with at most N configurations
 S is chosen by determining the impact on a
representative set of code segments C as follows:
S’ = default configuration + configurations with non-default
parameters
a) run C compiled with S’ on real hardware and retain in S’
only the valuable configurations
b) consider the combination of configurations in S’ as S’’
repeat a) for S’’ and retain only the best N configurations
repeat b) until no new configurations can be generated or the
speedup does not improve
November 11, 2003
10
OSE – Limiting the Search Space

Characterizing Configuration Correlations


build a optimization configuration tree
critical configurations = conf. at the same
level
1. Construct O = set of m most important
configurations in S for all
code segments in C
2. Choose all oi in O as the successor of the root
node.
3. For each configurations oi in O:
4. Construct Ci = {cj: argmax(pj,k) = i} k=1…m
5. Repeat steps 3, 4 to find oi successors limiting
the code segments to Ci and configurations to S\O.
November 11, 2003
11
OSE – Limiting the Search Space
 Compile-time search
 do a breadth first search on the optimization
configuration tree
 choose the configuration that yields the best
estimated performance
November 11, 2003
12
OSE – Limiting the Search Space
 Limit the OSE application
 to hot code segments
 hot code segments are identified through
profiling or hardware performance counters
during a program run
November 11, 2003
13
Evaluation

OSE Compiler Algorithm
1. Profile the code
2. For each Function:
3.
Compile to the high level IR
4.
Optimize using HLO
5. For each Function:
6.
If the function is hot:
7.
Perform OSE on second HLO and CG
8.
Emit the function using the best
configuration
9.
If the function is not hot use the
standard configuration
November 11, 2003
14
Compile-time Performance Estimation
 Model Based on:




Ideal Cycle Count – T
Data cache performance, Lambda, L
Instruction cache performance, I
Branch misprediction, B
November 11, 2003
15
Results
November 11, 2003
16
Conclusions
November 11, 2003
17